GTC EUROPE LOCATION

JENSEN HUANG

NVIDIA, Founder and CEO

ABOUT THE SPEAKER: Jensen Huang co-founded NVIDIA in 1993 and has served since its inception as president, chief executive officer and a member of the board of directors. Under his leadership, NVIDIA invented the graphics processing unit (GPU) in 1999. Since then, it has consistently set new standards in visual computing with breathtaking, interactive graphics available on devices ranging from smartphones and tablets to notebooks and workstations. NVIDIA's expertise in programmable GPUs has led to breakthroughs in parallel processing that make supercomputing inexpensive and widely accessible. The company holds more than 7,000 U.S. patents granted or pending, including ones covering designs and insights fundamental to modern computing. Huang is a recipient of the Dr. Morris Chang Exemplary Leadership Award from the Global Semiconductor Association in recognition of his exceptional contributions to driving the development, innovation, growth and long-term opportunities of the fabless semiconductor industry. He has received the Daniel J. Epstein Engineering Management Award from the University of Southern California and an honorary doctorate from Oregon State University. He was named to the U.S. Immigrant Entrepreneur Hall of Fame when it was established in 2012. Prior to founding NVIDIA, Huang worked at LSI Logic and Advanced Micro Devices. He holds a BSEE degree from Oregon State University and an MSEE degree from Stanford University.

Fundamentals of Deep Learning for Natural Language Processing

Prerequisites: Basic experience with neural networks.

In this course, you will receive hands-on training on the latest techniques for understanding textual input using Natural Language Processing. You’ll learn how to:

Classify words to accurately understand their meaning.

Handle factual queries and their semantic meaning.

Train Machine Translators from one language to another.

Upon completion of this course, you’ll be proficient in Natural Language Processing using neural networks in similar applications.

Deep Learning for Finance Trading Strategy

Prerequisites: Experience with neural networks and knowledge of the financial industry.

Linear techniques like principal component analysis (PCA) are the workhorses of creating ‘eigenportfolios’ for use in statistical arbitrage strategies. Other techniques using time series financial data are also prevalent. But now, trading strategies can be advanced with the power of deep neural networks.

In this course, you’ll learn how to:

Prepare time series data and test network performance using training and test datasets

Structure and train a LSTM network to accept vector inputs and make predictions

Use the Autoencoder as anomaly detector to create an arbitrage strategy

Upon completion, you’ll be able to use time series financial data to make predictions and exploit arbitrage using neural networks.

Level: Intermediate

Prerequisites: ‘Fundamentals of Deep Learning for Computer Vision’ or similar deep learning experience

Duration: 8 hours

Framework: TensorFlow

Deep Learning for Autonomous Vehicles - Perception

Prerequisites: Experience with CNNs.

In this course, you’ll learn how to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE™ PX development platform. Learn how to:

Integrate sensor input using the DriveWorks software stack

Train a semantic segmentation neural network

Optimize, validate, and deploy a trained neural network using TensorRT

Upon completion of this course, students will be able to create and optimize perception components for autonomous vehicles using DRIVE PX.

Level: Intermediate

Prerequisites: ‘Fundamentals of Deep Learning for Computer Vision’ or similar deep learning experience

Exposing accelerated application potential for concurrency and exploiting it with CUDA streams

Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

Exposing accelerated application potential for concurrency and exploiting it with CUDA streams

Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

Fundamentals of Deep Learning for Computer Vision

Prerequisites: Basic technical background

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this hands-on course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

Implement common deep learning workflows, such as image classification and object detection.

Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.

Deploy your neural networks to start solving real-world problems.

Upon completion, you’ll be able to start solving problems on your own with deep learning.

JEAN-LAURENT POITOU

The Big Trends in AI and How They are Affecting Companies

Explore the worldwide trend of implementing AI and Deep Learning solutions across a range of vertical markets as well as how they are a game-changing factor for the workflows of a company.

ABOUT THE SPEAKER: As the European Lead for Accenture's Applied Intelligence Services, Jean-Laurent Poitou helps Fortune 500 and public sector clients transform their business, applying artificial intelligence, data science, data engineering, robotics and automation to create new sources of revenue, provide outstanding digital customer experiences and leverage insights for competitiveness. During his 28 years at Accenture, Jean-Laurent has helped companies establish pan-European or global capabilities. He previously served as Accenture's Managing Director for Communications, Media & Technology (CMT), Growth & Strategy, championing the development and implementation of the CMT operating group strategy. Before taking that role, he was the Managing Director of Accenture's global Electronics & High Technology industry group. In this role, he developed and implemented Accenture's industry strategic programs and participated in larger deals for the practice, including the professional electronics, consumer electronics, aerospace, defense, semiconductor and software industries. Jean-Laurent holds an Engineering degree from Paris Ecole Polytechnique.

PROF. DR. DIRK PLEITER

JÜLICH SUPERCOMPUTING CENTRE, Research Group Leader

UNIVERSITY OF REGENSBURG, Professor of Theoretical Physics

GPU-Accelerated Computing at Scale

In pushing the limits of throughput of floating-point operations, GPUs have become a unique technology. During this session, we'll explore the current state of affairs from an application perspective. For this, we'll consider different computational science areas including fundamental research on matter, materials science, and brain research. Focusing on key application performance characteristics, we review current architectural and technology trends to derive an outlook towards future GPU-accelerated architectures.

ABOUT THE SPEAKER: Prof. Dr. Dirk Pleiter is the Research Group Leader at the Jülich Supercomputing Centre (JSC) and Professor of Theoretical Physics at the University of Regensburg. At JSC, he is leading the work on application-oriented technology development. Currently, he is also Principal Investigator of Exascale Labs at JSC and Work Package Leader within the Human Brain Project. He has played a leading role in several projects for developing massively-parallel special purpose computers, including several generations of QPACE. He is the author and co-author of more than 170 scientific papers, conference contributions and book chapters in the areas of theoretical high-energy physics and computer science.

ALISON KENNEDY

THE HARTREE CENTRE, Director

AI and HPC as Drivers for Industrial Competitiveness

In this talk, I will describe projects where AI is impacting our industry collaborators and outline my views on AI infrastructure based on our experiences to date. The Hartree Centre, a department of the UK National Labs, focusses on industry-led challenges in HPC, High Performance Data Analytics, and AI. Its mission is to make UK industry more competitive through the uptake of novel technologies. Historically our focus has been on HPC (simulation and modelling), and more recently on data centric computing. We are now seeing the convergence of the three technologies, and over the past couple of years, the focus has increasingly been on how AI can best be applied to add value for our industry partners.

ABOUT THE SPEAKER: Alison Kennedy is the Director of the Hartree Centre, based at the UK's Science and Facilities Council's National Laboratory at Daresbury. The Hartree Centre receives funding from BEIS with a remit to improve the global competitiveness of UK industry by facilitating the adoption of High Performance Computing (HPC), High Performance Data Analytics, and Cognitive Computing techniques by companies of all sizes. Prior to joining the Hartree Centre, she was the Executive Director of EPCC, the national HPC centre based at the University of Edinburgh, and recently completed a term of office as the Managing Director and Chair of the Board of Directors of PRACE (Partnership for Advanced Computing in Europe). She began her working life as a real time systems programmer in industry and has now worked in HPC for almost 25 years, managing large collaborative projects in HPC, Data and AI.

TIMO STICH

ZEISS, Senior Research Scientist

VISUHEALTH - Automated Diabetic Retinopathy Screening at ZEISS

We present how the ZEISS VISUHEALTH platform helps prevent blindness due to Diabetic Retinopathy. Instant screening results, continued screening, and early intervention are pivotal but challenging tasks globally due to limited access to retina specialists, especially in rural locations. ViSUHEALTH is our solution, with its remote and automatic grading of images taken with our non-mydriatic Fundus camera(s). Fundus images are uploaded and securely managed in the Cloud. Grading is offered by remote eye doctors or via our CE certified automated screening algorithm. The performance of the automated screening is on par with the manual results, but it also helps scale the screening capabilities to different needs. We will further discuss how we bring AI solutions from research prototypes to our products on the market.

ABOUT THE SPEAKER: Timo Stich is a Senior Research Scientist at ZEISS. His focus is on machine learning and computer vision. Prior to joining ZEISS he was Developer Technology Engineer for NVIDIA Corporation and Research Staff in Computer Graphics and Image Processing at the Max-Planck-Institute for Computer Science and the Computer Graphics Lab of Brunswick University, Germany. He received a diploma degree in Computer Science from Mannheim University, Germany and a Ph.D. degree from the Brunswick University, Germany.

ADRIANO DI FLORIO

CERN, Ph.D. Student

Machine Learning Techniques for Track Reconstruction at the CMS Experiment

Starting from 2020, the increased accelerator luminosity of the Large Hadron Collider at CERN will directly result in an increased number of simultaneous proton-proton collisions (pile-up) which will pose significant new challenges for the CMS experiment.

The adoption of machine learning techniques, along with traditional algorithms implemented on GPUs for both training and online inference, would reduce the incremented workload for the track reconstruction and improve the event selection.

ABOUT THE SPEAKER: Adriano Di Florio holds a Master's Degree in Nuclear, Subnuclear and Astroparticle Physics. Currently he is completing his Ph.D. in Physics and is a member of CMS collaboration. His main topics of study include multi-quark states including production, GPU/CUDA based tracking alghorithms, developing ML techniques for track seeds filtering, and GPU/GooFit interpolation algorithms applications.

CHRISTIAN HUNDT

JOHANNES GUTENBERG UNIVERSITY, MAINZ, Postdoctoral Researcher

ABOUT THE SPEAKER: Christian Hundt received his diploma degree in theoretical physics for the analysis of quantization maps and the associated structure of Lie groups on curved manifolds at the University of Mainz, Germany. In his current position, as a postdoctoral researcher at the Parallel and Distributed Architectures group, he investigates the design and parallelisation of elastic subsequence alignment algorithms using CUDA-enabled accelerators. Further topics of interest include metagenomic classification of DNA, image segmentation of medical data sets using deep learning and the efficient analysis of spatio-temporal data.

GÖKHAN YILDIRIM

ZALANDO, Senior Research Scientist

Fashion Design with GANs: Disentangling Color, Texture, and Shape

Explore a new approach in GPU-aided Fashion Design with Generative Adversarial Networks (GAN). We propose a deep-learning method that independently controls the color, texture, and shape of a generated clothing article. In this session, we will show how we disentangled the effect of input attributes by customizing a conditional GAN architecture with consistency-ensuring loss functions. Thanks to our GPU-based model, we will visually demonstrate rapidly-prototyped garments by designing and tuning their characteristics.

ABOUT THE SPEAKER: Gökhan Yildirim is a Senior Research Scientist at Zalando Research and develops Generative Adversarial Network-based methods for fashion design and visualisation. Gökhan finished his Ph.D. in the Image and Visual Representation Laboratory (IVRL), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. During his Ph.D., he worked on visual-saliency detection methods, which imitate human visual processing via biologically-inspired computer algorithms. After graduation, he pursued several deep learning topics, including image ranking and classification.

JEFFREY KELLING

HELMHOLTZ-ZENTRUM DRESDEN-ROSSENDORF, Scientist

ABOUT THE SPEAKER: Jeffrey Kelling is a Scientist in the computational science group at the Helmholtz-Zentrum Dresden-Rossendorf, concerned with high performance computing and deep learning applications in science. He obtained his diploma in statistical physics and wrote his Ph.D. thesis on massively parallel lattice Monte-Carlo simulations on GPUs.

MARTIN RAJCHL

IMPERIAL COLLEGE LONDON, IC Research Fellow

ABOUT THE SPEAKER: Martin Rajchl is an Imperial College Research Fellow with the Department of Computing and the Division of Brain Sciences at Imperial College London. His current research focuses on developing machine learning and computer vision methods for medical image analysis and assisted clinical decision support systems, with a particular focus on finding associations between cardiovascular health and changes in the brain, bridging the gap between machine learning and neuroscientific research. He has published over 90 clinical and technical articles, is a member of several programme committees (SPIE, MICCAI workshops) and holds numerous research awards and method challenge wins. Martin is the co-creator of several software libraries, most notably the Deep Learning Toolkit (DLTK) for Medical Imaging (https://dltk.github.io/).

MAXIMILIAN IGL

UNIVERSITY OF OXFORD, Doctoral Student

ABOUT THE SPEAKER: Maximilian Igl is a current doctoral student at the University of Oxford, researching autonomous intelligent machines and systems with a focus on inference techniques and deep reinforcement learning. He previously studied Physics, Economics and Technology Management in Munich at the Ludwig-Maximilians-Universität (LMU) and the Centre for Technology Management (CDTM).

ROSEMARY DOKOS

OXFORD NANOPORE, Director of Product Development

Current and Potential Healthcare Applications of Nanopore Technology

Oxford Nanopore has built the first and only real-time, portable DNA sequencer - the MinION. It is being used to bring DNA information to researchers in many sectors, including biomedical/cancer research, environmental monitoring, agriculture, food/ water testing, and education. Oxford Nanopore is using GPUs to make sure that genomic data can be processed in *real* real time, delivering potential benefits of rapid insights to users in any environment. Leila Luheshi and Rosemary Dokos will talk about current and potential healthcare applications of nanopore technology, and how GPUs will turn sequence data into rapid insights for disease or environmental management.

ABOUT THE SPEAKER: Rosemary Dokos is Senior Director, Product Management at Oxford Nanopore. The Product Management team at Oxford Nanopore Technologies enable the release of novel products and improvements into the field. The team is led by Rosemary who has over 10 years of experience in the Life Sciences market covering Genomics, Proteomics and Cell Biology.

SEPP HOCHREITER

INSTITUTE OF BIOINFORMATICS/LIT AI LAB, JOHANNES KEPLER UNIVERSITY LINZ, Head of Institute

Drug discovery is focused around finding relationships between chemical structure and biological effects of small molecules. Since such models depend on already-investigated chemical structures, they can hardly propose completely novel chemical scaffolds, a problem which has always been a drawback of traditional drug design. Now, researchers from Johannes Kepler University Linz, together with Janssen Pharmaceuticals, have found a novel way to discover drugs with GPU-based Deep Learning: instead of the chemical structure, they use images of cells that were treated with small molecules, and leverage deep neural networks to find relationships with biological effects. Thus, the image-based strategy can propose completely new chemical scaffolds, since there is no dependency on known, well-investigated chemical structures. In ongoing drug discovery projects, this novel GPU-driven strategy has identified many novel chemical scaffolds and thereby increased the discovery rate of drug candidates by 60 and 250-fold.

ABOUT THE SPEAKER: Sepp Hochreiter is a German AI researcher. Since 2006, he is the Head of the Institute of Bioinformatics at the Johannes Kepler University of Linz in Austria, and he is now also the Head of the LIT AI Lab. Sepp Hochreiter has made numerous contributions in the fields of machine learning and bioinformatics. He developed the long short-term memory (LSTM) for which the first results were reported in his diploma thesis in 1991. The main LSTM paper appeared in 1997 and is considered as a discovery that is a milestone in the timeline of machine learning. He applied biclustering methods to drug discovery and toxicology. He extended support vector machines to handle kernels that are not positive definite with PSVM model, and applied this model to feature selection, especially to gene selection for microarray data. In addition to his research contributions, Sepp Hochreiter is broadly active within his field - both locally in Austria and internationally.

MOHAMMAD ELGAMACY

MAX PLANCK INSITUTE FOR DEVELOPMENTAL BIOLOGY, Doctoral Candidate

Computational Design of Protein-Based Therapeutic Leads

Proteins are the main molecules responsible for information processing, catalysis and mechanical roles in living cells. Protein engineering allowed fine-tuning of the function and properties of natural proteins, leading to development of hundreds of therapeutic proteins. However, this approach offers very conserved and incremental navigation of the protein chemical space. Conversely, computational de novo protein design aims to offer a bottom-up approach for designing proteins of novel sequences and even folds, paving way to in silico design and experimental creation of proteins with arbitrary properties never before observed in nature. Together, by developing our methods for accelerated molecular dynamics sampling, we harness GPU computing power to design novel protein-based therapeutics. The final goal is to develop novel, pharmacologically active protein molecules with enhanced thermodynamic, kinetic and downstream production properties. Here we describe how we designed artificial cytokines in silico, and experimentally characterised their biophysical and pharmacological properties.

ABOUT THE SPEAKER: Mohammad ElGamacy is currently concluding his Doctoral degree in the field of protein design. A pharmacist by training, he did his Bachelor's studies in Pharmaceutical Science at Ain Shams University in Cairo. After working for a year in the fields of drug design and chemical biology, he moved to pursue his Master's degree in Molecular and Structural Biology at the Free University of Brussels and the Catholic University of Leuven. Currently, he is finalising his doctoral studies at the Max Planck Institute for Developmental Biology in Tuebingen.

IAIN WALLACE

ROVCO, Chief Technology Officer

ABOUT THE SPEAKER: As Rovco's CTO, Iain leads the development of 3D vision and AI to improve quality and reduce cost in subsea inspection. With a PhD in AI, and research in industry, his expertise ranges from multi-robot exploration to vision systems for Mars rovers. As well as delivering complex AI in the harshest environments on Earth, he has experience in 3D data visualisation, deep learning and embedded systems.

ELISA MAIETTINI

ISTITUTO ITALIANO DI TECNOLOGIA, Ph.D. Student

ABOUT THE SPEAKER: Elisa Maiettini is a Ph.D. student in deep learning for visual scene understanding in robotics at the Istituto Italiano di Tecnologia, iCub Facility, and the University of Genoa, Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS). She is under the supervision of Professor Lorenzo Natale and Professor Lorenzo Rosasco. Elisa graduated in software and electronics engineering from the University of Perugia, Italy, in 2013 and she obtained an M.D. with honours in software and automation engineering from the same university in 2016. Her fields of study are deep learning, computer vision and robotic visual perception, with her current focus on on-line object detection for robotic applications. The final goal of her Ph.D. is to provide robotic platforms, such as the iCub and R1, with the ability to understand scenes by exploiting visual inputs.

MINDAUGAS EGLINSKAS

Modern computing hardware and NVIDIA Jetson TX1 / TX2 performance create new possibilities for smart city applications and retail, parking lot, and drone industries. We'll present on how the PIXEVIA system covers vision processing and AI tasks using deep neural networks; learning using computer generated images for number plate recognition; and self-supervised learning for vehicle detection.

We will explore methods for orchestrating and combining information from different type of neural networks (from SSDs, Mask-RCNNs to attention based RNNs).

Real-world use cases for parking lots (empty parking space detection, number plate recognition) and retail industries (amount of stock on the shelf calculation, people counting with age and gender recognition) will also be presented.

ABOUT THE SPEAKER: Mindaugas Eglinskas is the CEO at PIXEVIA (formerly Magma Solutions) and has over 18 years of experience in computer vision, neural networks, and development of mission-critical systems. He lead a project for the Lithuanian Ministry of Defence to create a next-generation unmanned aerial vehicle prototype system. Mindaugas is also an assistant professor at Vilnius University, where his research focuses on neural networks and autonomous systems.

MAXIMILIAN BAUST

ABOUT THE SPEAKER: Maximilian Baust studied mathematics for science and engineering at the Technical University of Munich (TUM) and the Swiss Federal Institute of Technology Zurich from 2003 to 2008. Maximilian then joined the research group for computer aided medical procedures and augmented reality at TUM. In his Ph.D. thesis, he investigated variational methods for image segmentation and deformable image registration. In 2012, he joined ESG Elektroniksystem- und Logistik-GmbH, an SME in the area of defense and automotive technology, as a system engineer for camera-based driver assistance systems. From 2013 to 2017, Maximilian joined TUM for as a postdoctoral associate, where he supervised doctoral students and managed research projects in the areas of computer aided medical procedures, computer vision and machine learning. In 2017, Maximilian joined Konica Minolta Laboratory Europe as a senior R&D engineer and research specialist in the areas of medical image analysis, deep learning and robotics.

STAN BOLAND

FIVE AI INC., CEO

Building Europe's Answer to US and Chinese Hegemony in AVs

Tech companies are set to deliver safe AVs ahead of OEMs and tier vendors, but no one has a safe solution for our complex cities or European cities that are more complex than either the US or China. Five AI is Europe's fastest-growing tech company, with just under 100 people from 15 a year ago, created to deliver self-driving technology to Europe's city dwellers. This talk will be an opportunity to explain what challenges exist, how we are addressing them, what the progress in each field looks like, how we think services will be launched, and what remains to be done (which is a lot). We will include examples of how we are using NVIDIA technology to help us work on problems, show how we've configured our hardware, and discuss how we expect that to evolve from one platform to the next.

ABOUT THE SPEAKER: Stan Boland is a Co-Founder and CEO of Five AI Inc., a major venture-backed start-up developing driverless car technology to be delivered as a service. He was Co-Founder and CEO of two of Europe's most successful venture-backed communications silicon and software companies, Element 14 Inc. and Icera Inc., one bought by Broadcom and the other by NVIDIA, for an aggregate value of over $1 billion. Icera raised $245M in venture funding and grew to 320 staff before it was bought in 2011 and Element 14's team and technology now delivers over 80% of the world's broadband communications chips and software. Before that, Stan was CEO at computer pioneer Acorn and a Board Member at ARM. His early career includes spells at the aerospace firm Rolls-Royce and the computer company ICL. He is a graduate in Physics from the University of Cambridge, a Board member of the UK trade association TechWorks and a European council member of the Global Semiconductor Alliance (GSA).

LARS MESCHEDER

MAX PLANCK INSTITUT FOR INTELLIGENT SYSTEMS, Ph.D. Candidate

ABOUT THE SPEAKER: Lars Mescheder is a PhD candidate at the Max-Planck Institute (MPI) for Intelligent Systems in Tübingen. He is part of the Autonomous Vision Group under Prof. Andreas Geiger and is partially funded by Microsoft Research. Prior to joining the MPI, Lars Mescheder completed his undergraduate studies in mathematics and computer science at Braunschweig Institute of Technology, where he worked on combining optimisation-based and probabilistic image restoration algorithms. At the MPI, Lars Mescheder's research focuses on the probabilistic modeling of natural images using deep learning, both from a theoretical and practical perspective. He is particularly interested in gaining a better understanding of the training dynamics of Generative Adversarial Networks and to better understand the goals and principles of (unsupervised) representation learning.

DR. MICHAEL E. HAFNER

DAIMLER AG, Head of Automated Driving and Active Safety, Mercedes-Benz Cars Development

ABOUT THE SPEAKER: As the head of the 'Automated Driving and Active Safety' unit, Michael E. Hafner is responsible for the development of future driving functions on the road to autonomous driving at Mercedes-Benz Cars. His tasks comprise of the development of numerous driving assistance systems to production readiness such as, for example, the active distance control, steering, speed limit, and lane change assistance systems, as well as the various parking systems of all Mercedes-Benz passenger vehicles. In addition, he is responsible for the development of all future systems in the area of fully automated driving and self-driving vehicles.

Dr. Hafner studied electrical engineering and industrial information technology at Karlsruhe University and completed his doctorate in automation technology at Darmstadt Technical University. After joining Daimler in 2002, he held management positions with responsibility for on-board diagnostics and emissions certification before being appointed as an assistant to the Board of Management in Group Research and Mercedes-Benz Cars Development (RD). From 2010 to 2013, he headed the development of E/E brake control and suspension systems before taking over the Driver Assistance Systems and Active Safety unit. Since October 2016, he has also been responsible for all development tasks related to fully automated driving.

JÜRGEN SCHMIDHUBER

SWISS AI LAB, IDSIA, USI, Scientific Director

NNAISENSE, Co-Founder & Chief Scientist

World Models and AIs That Invent Their Own Goals

Since 2009, our deep learning artificial neural networks have won numerous contests in pattern recognition and machine learning. Today, they are used billions of times per day by the world's most valuable public companies. True AI, however goes far beyond slavishly imitating teachers through deep learning. That's why we have also focused, since 1990, on unsupervised AIs that invent their own goals and experiments to figure out how the world works and what can be done in it. Many of them model the world through a recurrent neural network that learns to predict the consequences of their action sequences. Without a teacher, they derive rewards from continually creating and solving their own, new, previously unsolvable problems, a bit like playing kids do, to become more and more like general problem solvers in the process. Relevant buzzwords include "artificial curiosity" (since 1990) and PowerPlay (since 2011). I will also briefly outline how AIs that set their own goals will eventually colonise the entire universe and make it intelligent.

ABOUT THE SPEAKER: Since 1987, Jürgen Schmidhuber has published on self-referential, self-improving Artificial Intelligence (AI) that learns to improve its own learning algorithm, to create an AI smarter than himself, then retire. The media have called him the father of AI. His lab's deep learning methods (such as LSTM) have revolutionized machine learning, are now available on 3 billion smartphones, and are used billions of times per day, e.g. for Facebook's automatic translation (4 billion times per day as of 2017), Google's speech recognition on 2 billion Android phones, Apple's Siri & QuickType on 1 billion iPhones, Amazon's Alexa, etc. His research group also established the field of mathematically rigorous universal AI and optimal universal problem solvers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He is the recipient of numerous awards including the 2016 IEEE Neural Networks Pioneer Award "for pioneering contributions to deep learning and neural networks,” Scientific Director of the Swiss AI Lab IDSIA (USI & SUPSI), and Chief Scientist of the company NNAISENSE, which aims to build the first practical general purpose AI.

RYAN HOOKS

HUXLEY, CEO

Augmented Reality for the Breeding and Optimization of Food and Medicine

Huxley is utilizing multispectral imaging via AR/AI to assist agronomists in the realm of plant phenotyping. Collaborating with Wageningen University and other top organizations, we are is creating real-time analysis of Controlled Environmental Agriculture. The global plant industry from food, flora, medicine, to the future of CRISPR, is currently is a 4 trillion dollar plus industry. The self-driving car ecosystem has 250+ companies, while plant sciences has very few. 2018 is the year that mobile GPU capabilities can give beginner labor in any language the ability to detect diseases early saving 5-10% of crops, while increasing production by up to 30%. With the same advanced greenhouse systems, China only gets 20% of the yield as a grower from the Netherlands. With AI/AR we aim to help the world grow Dutch. 98% less water, 98% less nutrient input, 2/3 less carbon emissions, while growing 10x per m2 with little to no pesticides. Labor adoption and retention will accelerate with the advancements of deep learning, creating food, medicine, and water security for all.

ABOUT THE SPEAKER: Ryan Hooks has worked at the top of the VFX industry in NYC for 8 years, www.mediadrift.com. 4 years ago he created www.avbl.com the "airBNB for skills", which led him to San Francisco where he started www.isabel.io. For two years he built greenhouses, hardware, and software to create pop-up refugee units that utilize 98% less water. Over the past year and a half he has ventured into AR/AI to help solve one of the greatest challenges to the scaling of efficient methods for plant growth. As of 2018 Huxley is part of Wageningen's Startlife program, with HQ based in Amsterdam. Utilizing his creative and technical competencies to connect the brightest plant scientists and programmers globally.

GEMMA VAN DER VOORST

UNIVERSITY OF GRONINGEN, Virtualisation Specialist

ABOUT THE SPEAKER: Gemma van der Voorst works at the University of Groningen as a Virtualisation Specialist. She loves to use creative solutions and uses out-of-the-box thinking to create cool virtualization and gpu solutions and always pushes them to the limit. She enjoys the challenges of bringing virtualised infrastructures to the next level.

WIETZE ALBERS

UNIVERSITY OF GRONINGEN, HPC Specialist

ABOUT THE SPEAKER: Wietze Albers works at the University of Groningen as a HPC specialist. He has been working in IT for a long time now and has a lot of experience. He thinks beyond what is possible and challenges himself to bring the infrastructure to the next level. He looks forward to exploring the new possibilities which will be made available with future technology.

EWA DÜRR

GOOGLE INC., Senior Manager Engineering for Cloud AI & Robotics

The Application for Deep Learning and Cloud AI Solutions in the Areas of Automation, Logistics, Manufacturing and Collaborative Robotics

This section will feature the application for Deep Learning and Cloud AI solutions in the areas of automation, logistics, manufacturing and collaborative robotics.

ABOUT THE SPEAKER: Ewa Dürr is a Product and Business Strategist in Accelerating Technologies. She is responsible for the strategy of the Google Engineering Center in Munich and advances Cloud AI engineering efforts in Robotics, Manufacturing and Automotive. Before Google, Ewa was an entrepreneur, leading product design and innovation at the tech start-up impacore and executed projects in Europe, LatAm and APAC as a strategy consultant with Bain & Co. Ewa holds an MBA from Harvard Business School and completed an executive program at the Singularity University. Interestingly, Ewa was once a professional orchestra violinist and is a passionate marathon runner (Paris 2017, NYC 2016, Berlin 2015, etc.).

DR. DAMIAN BORTH

UNIVERSITY OF ST. GALLEN, Director, Artificial Intelligence and Machine Learning Lab

Regulation, Identity, and Versioning of Deep Neural Networks

Deep Neural Networks (DNNs) demonstrated over the last years to successfully solve many different problems ranging from computer vision, speech recognition, natural language processing, machine translation, or anomaly detection. Given the continuous improvement of their performance more and more companies equip their products with deep neural networks serving as technology backend. Although, DNNs enable these companies to build never seen product before, we have to be aware that these products will interact with the real world and follow a regular product life cycle as any other technology. This leads to two implications: (1) first, how do we cope from a compliance point of view with tracking multiple versions of neural networks. (2) how can we technically define identity and versioning with respect to the nature neural networks and transfer learning?

ABOUT THE SPEAKER: Damian Borth is full professor at University of St.Gallen (HSG) and leading the Artificial Intelligence and Machine Learning Lab at the HSG. Previously, Damian was the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern and the PI of the NVIDIA AI Lab at the DFKI. Damian's research focuses on large-scale multimedia opinion mining applying machine learning and, in particular, deep learning to mine insights from online media streams. He is the scientific director of the Chartered Financial Data Science Program at "Deutsche Vereinigung für Finanzanalyse und Asset Management (DVFA)" and founding member of Sociovestix Labs, a social enterprise in the area of Financial Data Science. Damian did his postdoctoral research at UC Berkeley and ICSI in Berkeley. He received his PhD from University of Kaiserslautern.

DR. SADAF R. ALAM

SWISS NATIONAL SUPERCOMPUTING CENTRE (CSCS), Chief Technology Officer

ABOUT THE SPEAKER: Sadaf R. Alam is the Chief Technology Officer at the Swiss National Supercomputing Centre (CSCS) in Lugano, Switzerland. Dr. Alam studied computer science at the University of Edinburgh, UK, where she received her Ph.D. in 2004. Until March 2009 she was a computer scientist at the Oak Ridge National Laboratory, USA. In her role as CTO, she ensures end-to-end integrity of HPC systems and storage solutions as well as leading strategic projects at the centre.

PROF. SEBASTIEN OURSELIN

KING’S COLLEGE LONDON, Head of the School of Biomedical Engineering and Imaging Sciences

ABOUT THE SPEAKER: Professor Seb Ourselin is Head of the School of Biomedical Engineering and Imaging Sciences at King’s College London, which is dedicated to the development, clinical translation and clinical application of medical imaging, computational modelling, minimally invasive interventions and surgery. He is Director of the Wellcome / EPSRC Centre for Interventional and Surgical Sciences and the EPSRC Image-Guided Therapies UK Network+ and has raised over £40M as Principal Investigator, including £10M under the Innovative Engineering for Health initiative to create the GIFT-Surg project.

He is co-founder of Brainminer, an academic spin-out commercialising machine learning algorithms for brain image analysis. Their first product, DIADEM, a clinical decision support system for dementia diagnosis, is CE marked and medically approved.

He has published over 400 articles and is an associate editor for IEEE Transactions on Medical Imaging, Journal of Medical Imaging, Nature Scientific Reports, and Medical Image Analysis. He has been active in conference organisation (12 international conferences as General or Program Chair) and professional societies (APRS, MICCAI). He was elected Fellow of the MICCAI Society in 2016.

Previously, he was based at UCL where he had numerous affiliations including Director of the Institute of Healthcare Engineering and the EPSRC Centre for Doctoral Training in Medical Imaging, Vice-Dean (Health) for the Faculty of Engineering Sciences, Head of the Translational Imaging Group within the Centre for Medical Image Computing (CMIC) and Head of Image Analysis at the Dementia Research Centre (DRC). Before joining UCL, he founded and led the CSIRO BioMedIA Lab, Australia. He led the imaging research programme of the AIBL study and of a successfully commercialized colonoscopy simulator.

PACKAGE COMPARISON

Conference + Training

Conference

EXHIBITS

Pre-conferenceTraining

Conference Sessions

Training / Hands-on Labs(up to 3 per day)

Talks and Tutorials

Keynotes

Exhibits

Lunch

Evening Receptions

GTC Party1

Conference Materials2

Pre-GTC DLI Workshop(Sunday, March 25)

1. Additional GTC Party passes available for purchase for a spouse/guest with a paid registration. Available as add-on for Exhibits Only or One-Day (other than Wednesday, 3/28) pass holders. GTC Party pass valid for access to party on Wednesday, 3/28 only; does not include any other conference privileges.

2. Conference materials include access to post event recordings and presentation files, and a GTC-branded bag and t-shirt.